import streamlit as st
import folium
from folium.plugins import MarkerCluster
from streamlit_folium import folium_static
import googlemaps
from datetime import datetime
import os

# Initialize Google Maps
gmaps = googlemaps.Client(key=os.getenv('GOOGLE_KEY'))

# Function to fetch directions
def get_directions_and_coords(source, destination):
    now = datetime.now()
    directions_info = gmaps.directions(source, destination, mode='driving', departure_time=now)
    if directions_info:
        steps = directions_info[0]['legs'][0]['steps']
        coords = [(step['start_location']['lat'], step['start_location']['lng']) for step in steps]
        return steps, coords
    else:
        return None, None

# Function to render map with directions
def render_folium_map(coords):
    m = folium.Map(location=[coords[0][0], coords[0][1]], zoom_start=13)
    folium.PolyLine(coords, color="blue", weight=2.5, opacity=1).add_to(m)
    return m

# Function to add medical center paths and annotate distance
def add_medical_center_paths(m, source, med_centers):
    for name, lat, lon, specialty, city in med_centers:
        _, coords = get_directions_and_coords(source, (lat, lon))
        if coords:
            folium.PolyLine(coords, color="red", weight=2.5, opacity=1).add_to(m)
            folium.Marker([lat, lon], popup=name).add_to(m)
            distance_info = gmaps.distance_matrix(source, (lat, lon), mode='driving')
            distance = distance_info['rows'][0]['elements'][0]['distance']['text']
            folium.PolyLine(coords, color='red').add_to(m)
            folium.map.Marker(
                [coords[-1][0], coords[-1][1]],
                icon=folium.DivIcon(
                    icon_size=(150, 36),
                    icon_anchor=(0, 0),
                    html=f'<div style="font-size: 10pt; color : red;">{distance}</div>',
                    )
                ).add_to(m)

# Driving Directions Sidebar
st.sidebar.header('Directions 🚗')
source_location = st.sidebar.text_input("Source Location", "4 Brotherton Way, Auburn, MA 01501")
destination_location = st.sidebar.text_input("Destination Location", "366 Shrewsbury Street, Worcester, MA, 01604")

# Fetch and Display Directions
if st.sidebar.button('Get Directions'):
    steps, coords = get_directions_and_coords(source_location, destination_location)
    if steps and coords:
        st.subheader('Driving Directions:')
        for i, step in enumerate(steps):
            st.write(f"{i+1}. {step['html_instructions']}")
        st.subheader('Route on Map:')
        m1 = render_folium_map(coords)
        folium_static(m1)
    else:
        st.write("No available routes.")

# Massachusetts Medical Centers
st.markdown("### 🗺️ Maps - 🏥 Massachusetts Medical Centers 🌳")
m2 = folium.Map(location=[42.3601, -71.0589], zoom_start=8)
marker_cluster = MarkerCluster().add_to(m2)

massachusetts_med_centers = [
    ('The Endoscopy Center', 42.2098, -71.8356, '4 Brotherton Way, (508) 425-5446', 'Auburn'),
    ('ReadyMED – Auburn', 42.2090, -71.8358, '460 Southbridge Street, (508) 595-2700', 'Auburn'),
    ('Durable Medical Equipment', 42.2115, -71.8370, '42 Southbridge Street, (508) 407-7700', 'Auburn'),
    ('Auburn', 42.2098, -71.8356, '4 Brotherton Way, (508) 832-9621', 'Auburn'),
    ('Framingham', 42.2793, -71.4162, '761 Worcester Rd, (508) 872-1107', 'Framingham'),
    ('Holden', 42.3518, -71.8634, '64 Boyden Road, (508) 829-6765', 'Holden'),
    ('ReadyMED – Hudson', 42.3912, -71.5662, '234 Washington Street, (508) 595-2700', 'Hudson'),
    ('ReadyMED – Leominster', 42.5251, -71.7598, '241 North Main Street, (508) 595-2700', 'Leominster'),
    ('Leominster', 42.5204, -71.7717, '225 New Lancaster Road, (978) 534-6500', 'Leominster'),
    ('ReadyMED – Milford', 42.1487, -71.5152, '340 East Main Street, (508) 595-2700', 'Milford'),
    ('Milford', 42.1398, -71.5163, '101 Cedar Street, (508) 634-3100', 'Milford'),
    ('The Surgery Center', 42.2924, -71.7131, '151 Main St, (844) 258-4272', 'Shrewsbury'),
    ('Shrewsbury Occupational Medicine', 42.2930, -71.7240, '222 Boston Turnpike, (508) 853-2854', 'Shrewsbury'),
    ('Shrewsbury', 42.2865, -71.7147, '378 Maple Ave, (508) 368-7820', 'Shrewsbury'),
    ('Southborough', 42.3057, -71.5256, '24-28 Newton Street, (508) 481-5500', 'Southborough'),
    ('Webster', 42.0474, -71.8801, '344 Thompson Road, (508) 671-4050', 'Webster'),
    ('Westborough', 42.2695, -71.6161, '900 Union Street, (508) 366-8836', 'Westborough'),
    ('Worcester – Saint Vincent Cancer and Wellness Center', 42.2626, -71.8027, '1 Eaton Place, (508) 368-5430', 'Worcester'),
    ('Worcester – Neponset Street', 42.2614, -71.8007, '5 Neponset Street, (508) 368-7800', 'Worcester'),
    ('Worcester Medical Center', 42.2614, -71.8006, '123 Summer Street, (508) 852-0600', 'Worcester'),
    ('Worcester – Harding Street Rehabilitation & Sports Medicine', 42.2605, -71.8000, '112 Harding Street, (508) 964-5592', 'Worcester'),
    ('Worcester – Gold Star Boulevard Rehabilitation and Sports Medicine', 42.2910, -71.7999, '50 Gold Star Boulevard, (508) 856-9510', 'Worcester'),
    ('Worcester – Front Street', 42.2619, -71.8008, '100 Front Street, (508) 595-2000', 'Worcester'),
    ('Surgical Eye Experts', 42.2620, -71.8029, '385 Grove Street, (508) 453-8802', 'Worcester'),
    ('ReadyMED PLUS – Worcester', 42.2612, -71.8010, '366 Shrewsbury Street, (508) 595-2700', 'Worcester')
]


# Dropdown to select medical center to focus on
medical_center_names = [center[0] for center in massachusetts_med_centers]
selected_medical_center = st.selectbox("Select Medical Center to Focus On:", medical_center_names)

# Zoom into the selected medical center
for name, lat, lon, specialty, city in massachusetts_med_centers:
    if name == selected_medical_center:
        m2 = folium.Map(location=[lat, lon], zoom_start=15)

# Annotate distances and paths for each medical center
add_medical_center_paths(m2, source_location, massachusetts_med_centers)

folium_static(m2)

def Fairness():
    # List of 10 Types of Bias 😓
    st.markdown("### 10 Types of Bias in Geographical Healthcare Data 👩‍⚕️🌍")
    st.markdown("""
    1. **Sampling Bias**: When the clinics or medical centers chosen for analysis do not represent the entire population.
    2. **Confirmation Bias**: Picking clinics or centers that confirm pre-existing assumptions.
    3. **Location Bias**: Focusing only on urban or rural areas.
    4. **Temporal Bias**: Not considering the seasonality or time-sensitive factors.
    5. **Accessibility Bias**: Overlooking clinics that are hard to reach but may offer unique specialties.
    6. **Economic Bias**: Focusing only on wealthy areas.
    7. **Size Bias**: Ignoring smaller clinics or new centers.
    8. **Technology Bias**: Assuming higher tech facilities provide better care.
    9. **Specialization Bias**: Overemphasis on one type of specialty.
    10. **Reporting Bias**: Basing judgments on self-reported data without validation.
    """)

    # List of 10 Types of Fairness 😇
    st.markdown("### 10 Types of Fairness in Geographical Healthcare Data 🌍👩‍⚕️")
    st.markdown("""
    1. **Geographical Fairness**: Equal representation of urban and rural areas.
    2. **Socioeconomic Fairness**: Diverse economic statuses in the sample.
    3. **Healthcare Need Fairness**: Clinics catering to various healthcare needs.
    4. **Accessibility Fairness**: Including centers reachable by public transportation.
    5. **Specialization Fairness**: A balanced view across various medical specialties.
    6. **Temporal Fairness**: Data that accounts for seasonal or time-sensitive changes.
    7. **Cultural Fairness**: Inclusion of centers serving diverse cultural communities.
    8. **Demographic Fairness**: Representation across different age groups and genders.
    9. **Quality of Care Fairness**: Balanced data on patient satisfaction and quality of care.
    10. **Resource Allocation Fairness**: Fair distribution of resources among different centers.
    """)

Fairness()

def Fairness2():
    st.title("Bias and Fairness in Geographical Healthcare Data 🌍👩‍⚕️")

    st.markdown("### 10 Types of Bias in Geographical Healthcare Data 👩‍⚕️🌍")
    bias_types = {
        "Sampling Bias": r"\frac{\text{Unrepresented Population}}{\text{Total Population}}",
        "Confirmation Bias": r"\frac{\text{Data Confirming Assumptions}}{\text{Total Data Points}}",
        "Location Bias": r"\left| \frac{\text{Urban Centers}}{\text{Rural Centers}} - 1 \right|",
        "Temporal Bias": r"\frac{\text{Time-Sensitive Data Ignored}}{\text{Total Data Points}}",
        "Accessibility Bias": r"\frac{\text{Inaccessible Clinics}}{\text{Total Clinics}}",
        "Economic Bias": r"\frac{\text{Wealthy Area Clinics}}{\text{Total Clinics}}",
        "Size Bias": r"\frac{\text{Ignored Small Clinics}}{\text{Total Clinics}}",
        "Technology Bias": r"\frac{\text{High-Tech Clinics}}{\text{Total Clinics}}",
        "Specialization Bias": r"\frac{\text{Overemphasized Specialties}}{\text{Total Specialties}}",
        "Reporting Bias": r"\frac{\text{Unvalidated Reports}}{\text{Total Reports}}"
    }
    
    for bias, formula in bias_types.items():
        st.markdown(f"**{bias}**")
        st.latex(f"{formula}")
        
    st.markdown("### 10 Types of Fairness in Geographical Healthcare Data 🌍👩‍⚕️")
    fairness_types = {
        "Geographical Fairness": r"1 - \left| \frac{\text{Urban Centers}}{\text{Rural Centers}} - 1 \right|",
        "Socioeconomic Fairness": r"\frac{\text{Diverse Economic Clinics}}{\text{Total Clinics}}",
        "Healthcare Need Fairness": r"\frac{\text{Various Healthcare Need Clinics}}{\text{Total Clinics}}",
        "Accessibility Fairness": r"\frac{\text{Accessible Clinics}}{\text{Total Clinics}}",
        "Specialization Fairness": r"1 - \left| \frac{\text{Specialized Clinics}}{\text{General Clinics}} - 1 \right|",
        "Temporal Fairness": r"1 - \frac{\text{Time-Sensitive Data Ignored}}{\text{Total Data Points}}",
        "Cultural Fairness": r"\frac{\text{Diverse Cultural Clinics}}{\text{Total Clinics}}",
        "Demographic Fairness": r"\frac{\text{Diverse Demographic Clinics}}{\text{Total Clinics}}",
        "Quality of Care Fairness": r"\frac{\text{High-Quality Clinics}}{\text{Total Clinics}}",
        "Resource Allocation Fairness": r"\frac{\text{Evenly Distributed Resources}}{\text{Total Resources}}"
    }
    
    for fairness, formula in fairness_types.items():
        st.markdown(f"**{fairness}**")
        st.latex(f"{formula}")

if __name__ == "__main__":
    Fairness2()